Why Your Loyalty Program Needs an Actuary

Loyalty programs are designed for continuous participation from members.


But if you’re a loyalty program manager responsible for generating program ROI, it’s not enough to simply understand where you are — you need to know where you’re going.


Unless you have a crystal ball, however, you’re likely going to need some help. But where should you start?


The answer is predictive modeling. Predictive modeling, which uses statistics to predict future outcomes, helps program managers determine the value each loyalty program member will bring to a company. Unfortunately, accurately forecasting the right numbers is easier said than done.


That’s where actuaries come in.


While their title may not be the most glamorous, actuaries are the financial experts who can shed light on the future of your loyalty program.


How exactly? By forecasting and extrapolating off past and current data, often with great accuracy. In fact, employing actuarial insights is one of the best ways to prepare your program for long-term financial success.


Actuaries can help you answer important questions such as:


“How can I optimize member acquisition?”

“Who are my highest value members?”

“Is my program strategy driving incremental value?”


When you arm yourself with an actuary, you’re taking an essential step towards setting and achieving your loyalty program goals.


In this piece, we’ll explain:

  • What actuaries actually do
  • 9 reasons why actuarial insight is useful to your loyalty program
  • Why a data science team isn’t enough
  • What you should look for in a loyalty program actuary


Do you know where your program is heading and why? Let’s enter the world of actuarial insight.


What does an actuary actually do?


To those new to the world of actuarial consulting, actuaries are somewhat of an enigma.


The Society of Actuaries labels the profession as “Part super-hero. Part fortune-teller. Part trusted advisor.” While you probably won’t encounter an actuary with a cape, these professionals take on an essential role in accounting for loyalty programs: they manage loyalty program risk by planning for the future and protecting their organization against loss.


Actuaries are generally associated with the insurance industry

Most people associate actuaries with insurance, and they’re right: over 50% of actuaries are employed directly or indirectly by the insurance industry.


Insurance companies are in the business of predicting outcomes. These companies are required to reserve cash for the policies that they sell, and must set aside money for the claims they expect to eventually pay out on those policies. Given the range of insurance policies, this planning can take on many different forms.


For example, when an insurance company issues your car insurance policy, they have to estimate the probability of paying a claim, the size of that claim, and the timing of when it will occur.


You pay your policy annually. And maybe someday (hopefully not), you’ll need to draw on your insurance policy to cover the costs of an accident or damage to your vehicle. The insurance company has to ensure, on average, customers are paying more into the plan than the company is doling out. It’s how they stay in business while still providing uninterrupted coverage.


Another example is workers’ compensation insurance. Some claims may not be reported until many years later (such as a disease brought about by occupational conditions), but would require medical payments throughout the rest of a person’s life.


While auto insurance claims only involve predictions over the twelve months of the policy period, reserving for worker compensation requires the tools to predict expected claims payments decades into the future based on the information available today.


When it comes to your loyalty program, this is just the type of information you need.


Actuaries have the toolbox for making long-term predictions

Fortune-tellers have crystal balls; actuaries have numbers, data, and statistics. Using statistical analysis, they can evaluate the probability of future events occurring and plan accordingly.


In the context of loyalty programs, cash flow (amount, timing) is the primary metric examined. Other metrics can include:

  • Customer lifetime value (CLV)
  • Customer future value (CFV)
  • Customer potential value (CPV)
  • Ultimate redemption rate (URR)
  • Cost-per-point (CPP)
  • Breakage


Forecasting over the long term is required for insurance companies, and can be highly beneficial for loyalty programs. Let’s walk through the benefits to long-term forecasting with actuaries.


9 reasons why actuarial insight is useful for loyalty programs

actuarial insight for loyalty programs


1. Helps predict loyalty program liability

When points are issued, companies must defer revenue for the eventual cost of redemption. This is required by the financial reporting standards ASC 606/IFRS 15. The challenge in estimating liability, however, lies in the number of variables associated with estimating redemption costs.


Actuaries can predict what percentage of points will ultimately be redeemed, when those redemptions will occur, and how much those redemptions will cost the company. After all, points can be redeemed many years (potentially decades) after they are earned. The best estimates require long-term forecasts.


Adding to this challenge is the reality that loyalty programs are dynamic, constantly evolving entities:

  • Members change their behavior frequently
  • Members are becoming increasingly sophisticated thanks to the availability of online resources on loyalty programs
  • Programs frequently offer deals and marketing campaigns
  • Programs occasionally change program structure – e.g., expiration rules, award charts, accrual charts, etc.


Liabilities can be very large, in the hundreds of millions or even billions of dollars – particularly among frequent flyer and hospitality programs. At this scale, CFOs and auditors require proper due diligence to validate liability estimates. Actuarial opinions provide formal documentation from a credentialed actuary stating their professional opinion on the booked program liability. This provides CFO, auditors, and other stakeholders proof of the due diligence.


2. Quantifies customer lifetime value (CLV)

While liability is important, the whole point of a loyalty program is to increase customer brand loyalty. (i.e., customers should keep coming back to your company to make purchases). The best way to quantify the value of this long-term loyalty is a metric known as customer lifetime value (CLV). CLV predicts the profits that a given member is expected to generate over his lifetime in the program.

As we’ve discussed in previous articles, CLV and its family of metrics are the most important KPIs to link program management to economic value creation. In fact, you can even use these metrics to proactively create economic value (you can review how to calculate customer lifetime value here).

Properly calculating CLV is critical to loyalty program profitability and requires the ability to predict over long horizons. As you’ll see, CLV and its many applications will unify the rest of this section.


3. Identifies high value members

Simply put, members with high CLV are likely to spend more money with your company. Targeting them with special offers will make them feel appreciated and inspire even more purchases, maximizing the likelihood of unlocking their expected future profits.


4. Drives incremental value by targeting high potential members

With just a small nudge, high potential members will likely increase their program participation, and, as a result, their CLV. A well-executed, targeted incentive strategy can grow the future economic value of your loyalty program.


5. Targets at-risks members with decreasing CLV

On the flip side, loyalty programs can use CLV as a defensive strategy. By identifying leading indicators of declining participation, programs can be designed to proactively prevent loss of economic value.


6. Provides enhanced customer service based on CLV

CLV quantifies the value of each customer. This indispensable information enables customer service agents to focus their efforts and spend more time and energy on resolving issues for those members most valuable to the organization. Note that this doesn’t mean that members with a low CLV don’t matter; rather, it focuses efforts on providing services that will keep your most loyal customers happy.


7. Optimizes new member acquisition based on value rather than cost

Well-run loyalty programs can become an appreciating asset for any company. Examining CLV as a growth value metric ensures that every dollar spent on acquisition is spent in a way that maximizes economic value, rather than minimizing cost.


8. Optimizes program design

Actuaries conduct scenario testing to forecast the impact of various program changes on CLV. By quantifying how these changes impact CLV, program managers can identify and implement the changes that drive the most growth. This ensures that program structure changes are focused on maximizing economic value.


9. Aligns marketing and finance around a common set of KPIs to measure economic value creation

Miscommunication and lack of alignment leads to lost time in business. Aligning KPIs across organizations prevents organizational conflict. Using the shared language of CLV, organizations can increase the speed by which they make decisions — a competitive advantage in today’s fast paced world.


Why a data science team isn’t enough

In today’s digital world, data analysis is a highly sought after skill. Wherever there’s a large amount of data, data scientists are needed to plug and chug through significant statistical analyses, searching for patterns and identifying solutions.


At first glance, data scientists share many skills with actuaries. Both require business intelligence and data analysis skills. And the end result of data analysis and actuarial insight is a best-fit solution to an ambiguous problem.


However, these professions are not interchangeable.


Data scientists lack domain knowledge

Data scientists know how to build models to make predictions, but not necessarily predictions over long horizons, which is where actuaries excel. Actuaries are also subject to rigorous formal training and credentialing in their domain of focus – whether that be insurance, finance, investments, etc.


We can’t overstate the value of domain knowledge.  Consider this example:

Suppose you need brain surgery. Are you going ask your family doctor to do the operation, or a trained brain surgeon? Both are very smart, both are trained in the medical profession, and both probably have the aptitude to do it. But only the brain surgeon has the training, experience, and specific domain knowledge required to do the job correctly.


Data scientists and actuaries are similar: both are smart, both are trained in applied statistics, and both have the aptitude to learn to predict over long horizons; but only actuaries have the explicit training, experience, and specific domain knowledge required to do the job right.


What makes an ideal loyalty program actuary?

Traditional actuarial models focus on making predictions in aggregate. But the value of CLV lies in its ability to make predictions at the individual member level.


Within actuarial consulting there is even more niche domain knowledge required to properly apply actuarial theory to loyalty programs. The most effective professionals combine predictive modeling with expert financial reporting knowledge.


Loyalty Program Actuary - Venn Diagram


Individual predictive modeling

Loyalty program actuaries need to know how to leverage today’s technology to build predictive models. Traditional actuarial methods were developed decades ago, well before the availability of today’s big data and advanced predictive modeling capabilities.


While the basic actuarial concepts underlying traditional methods remain applicable to today’s loyalty programs, the methods themselves need to be redesigned to leverage modern technology and allow for individual member-level predictions. Therefore, you need an actuary with strong predictive modeling skills and experience using modern technology to solve problems.


Financial reporting expertise

Given a loyalty program’s complex financial reporting requirements, you not only need an actuary with predictive modeling skills, but also one with a strong understanding of the business dynamics and financial reporting regulations surrounding loyalty programs.


ASC 606/IFRS 15 have introduced updated revenue recognition standards and a fresh focus on loyalty program accounting practices. Your actuary needs to know how to properly identify performance obligations when accounting for loyalty programs.

In summary, the ideal loyalty program actuary is adept at predicting behavior over the long term, can build predictive models at the individual member level, and has a nuanced understanding of business dynamics and the financial reporting environment. This combination enables actuaries and loyalty program leaders to translate predictions into real business insight and loyalty program value.


Optimize your program with the right actuary

loyalty program optimization


Loyalty programs generate more than a significant amount of data. Between engagement metrics, membership statistics and financial results, programs need someone with the skills to discern the meaning amidst all the noise. And while it’s easy enough to turn to any data scientist or actuary, to get the most actionable insight from your data, it’s best to trust those with deep domain expertise.


Imagine if each year at tax season, you hired an average accountant with limited personal tax experience. Would you really expect him or her to optimize your taxes and bring you the biggest refund?


The odds are that you would sleep better at night (and receive the biggest bang for your buck) if you hired a professional tax accountant with years of experience.


An ideal actuary can provide just as much peace of mind — and financial return — to your loyalty program. Select an expert who understands how to model your loyalty program liability, the nuances of customer lifetime value, and the fundamentals of optimizing program ROI.


Loyalty programs are one of the most effective ways of driving long-term customer value and ensuring the continued success of your business. When it comes to your program’s financial management, why cut corners?




KYROS provides sophisticated predictive analytics solutions that help companies optimize the financial performance of their loyalty program. Need an experienced set of eyes? Contact us for a free consultation.


A Professional’s Guide to Loyalty Program Liability

To the great delight of customers, many companies offer loyalty programs. These programs allow customers to receive rewards for the purchases they make, with repeated purchases from the same company resulting in an ever-increasing, compounding array of incentives and kickbacks. Customers become motivated to direct as many of their purchases as possible towards the same organization, and businesses reap the rewards of more purchases and a loyal customer base.  It’s the perfect win-win scenario.


Except when it’s not.


While customer loyalty programs are a tried-and-true method of drumming up consistent business, potential risks must be carefully considered when implementing one into your company’s marketing framework. Loyalty programs can result in more sales, but they also carry what is known as loyalty program liability.


Loyalty program liability is the eventual cost to your company of the redemption of all outstanding loyalty points. If accounted for properly, they can be an effectively-wielded strategy for increasing customer engagement and strengthening the consistency of your company’s relationships with clients.


Conversely, failure to properly factor in the impact of these material financial costs  on your company’s balance sheet can have an unexpected financial cost upon redemption of outstanding rewards points.


Fortunately, these financial risks can be mitigated using careful planning and sophisticated analytics tools.  A loyalty program should be viewed as an investment, and, when prudently executed, can return far more than what it cost to implement.


Read on to find out how your company can leverage the benefits of loyalty programs while limiting the risks associated with loyalty program liability.


The basics of loyalty program liability

The impact of customers redeeming loyalty rewards is a balance sheet liability that can cost companies billions of dollars.

Loyalty Program Liability Basics

Though structures vary, the essence of a loyalty program is this: A company offers its clients a certain amount of “currency” per every unit of a designated dollar amount spent. In practice, this might look like Walmart offering shoppers 20 rewards points for every $10 spent, or a pet store offering one “Barky Buck” for every three cans of dog food purchased.


Of course, these currencies mean nothing if they’re not able to be redeemed for products or services, so the second part of the loyalty program formula is to allow customers to redeem the accrued currency for company offerings. Many times, these offerings are simply free or reduced inventory items, but often, the most valued (and desired) options can only be attained by earning enough of the loyalty program’s currency.


In each case, companies are forced to eventually assign the currency real value by making it exchangeable for tangible items. In turn, the delivery of these items in exchange for the rewards points comes at a cost to the company.


For example, that free, steaming hot cup of coffee given by Starbucks to the loyal client actually costs Starbucks some big money. While a single cup doesn’t amount to much, multiply it by the millions of Starbucks customers getting free coffees and the cost skyrockets. And what is this cost known as? That’s right —  loyalty program liability.


What loyalty program liability means to your company

All liabilities matter, and loyalty program liability can impact both the financial health of an organization and the way it’s perceived by the market.


Loyalty Program Liability - Points


The principle reason why loyalty program liability matters is that because, like any other variety of corporate liability, it can negatively impact the financial standing of a company.


The most direct way it can harm the financial health of a company is when companies opt to operate on a model that overestimates breakage. Breakage is the accounting world’s way of describing services that are paid for by a customer but not actually used.


A classic example of this is the sweeping tide of gym memberships that get activated at the beginning of every year by inspired would-be gym goers, bent on finally keeping their New Year’s resolution.


Similarly, every year companies make millions off of unused gift cards for which money is paid, but no products are consumed. While breakage can result in unanticipated profits, relying on it solely to underwrite unsustainable advertisement promises can have devastating effect on a company.


Changes in regulations concerning how companies must classify rewards points are also certain to heighten the impact of loyalty program liability. As of 2018, the International Finance Reporting Standard (IFRS) and US GAAP has mandated that companies categorize rewards points as deferred revenue, considering them separate parts of a sale. This signifies that, at least initially, companies will have to decrease their listed profits from whatever they’ve actually generated to the smaller amount that results after the value of the accompanying rewards points is subtracted. This is particularly true in the US, where the the change in accounting rules is more dramatic.


Although this doesn’t mean that companies cannot eventually incorporate the profits earned from breakage after points expire into their bottom lines, it does mean that, at least in the short term, the value of rewards points must be factored into reports of revenue. For any company, depressions in revenue reports are an important concern, as they affect investor confidence and can change the market valuation of the organization.


Bottom line

Like any other type of liability, loyalty program liability can affect the financial well-being of a company. Due to new regulations, businesses will now be forced to view rewards points as independent occurrences from the event that incurred them, and investors will view them as revenue deferred. This means that rewards points can bring down the revenue reports of a company at any given moment, even if, eventually, they come to increase them.


Most importantly, however, effectively managing loyalty program liability requires measured, strategic, interdepartmental cooperation between accounting, financial and marketing departments — which is where we now turn our attention.



Loyalty program liability accounting

Accounting departments need to accurately hone in on ultimate redemption rates and costs per point to correctly quantify outstanding levels of loyalty program liability.

Loyalty Program Liability Accounting

Accounting departments are pivotal to the management of loyalty program liabilities. After all, in order to properly calculate the direction in which loyalty program liabilities are heading, you need to know where they stand today.


For many of the largest loyalty programs, these liabilities can amount to billions of dollars:  


Deferred revenue liabilities from loyalty programs (2017)

CompanyDeferred revenue liabilities
American Express$7.751 billion
Marriott$4.940 billion
United$4.741 billion
Delta$4.118 billion
American Airlines$2.777 billion
Southwest Airlines$1.676 billion
Hilton$1.461 billion
Intercontinental Hotels$760 million


At this scale, even small changes in redemption behavior can drive significant financial impact. For example, if a $1 billion liability needs to be restated by just one percent, that will drive a $10 million hit to income during the period in which the liability is restated.


Proper understanding of the ultimate redemption rate (URR) as well as the cost per point (CPP), is key to getting the pulse of existing liabilities. While many companies believe that URR cannot be properly gauged, the reality is that this rate can be determined with a fair degree of accuracy.  What tends to impede companies from correctly evaluating their URR is their neglect of many valuable data points concerning the individual behaviors of their members.


The previous actions of loyalty members can help predict what they’ll do in the future, and by analysing these individually, companies can develop forward-looking databases that can give cogent insights on how likely individual point-bearers are to redeem the points.  


While this may require the analysis of huge quantities of data points across a large membership base, new techniques are making it easier for companies to wrangle this “big data” and uncover hidden insights. In particular, the combination of actuarial science and machine learning has proven to be a robust approach to predicting redemption behavior.


Financial reporting not only requires an estimate of the liability, but also disclosures about the timing of when the obligations will be fulfilled. This adds another dimension of complexity to the models, since the models must estimate the total number of points that will redeem as well as the timing of when they will burn.


Unfortunately, the methods companies use to estimate URR are often too simplistic to make accurate predictions of redemption behavior in the dynamic world of loyalty programs, and can result in materially biased estimates. These methods include approaches that look solely at aggregated historical data, or analysis by member vintage.


A URR estimate biased high means that you expect more redemptions to occur than actually will. This can result in deferring too much revenue, and never seeing the number of redemptions required to allow you to eventually recognize it. In essence, the revenue is “stuck” in the deferred revenue account.


A URR estimate biased low means that more redemptions will occur than you expect. When these redemptions occur, you may find that you don’t have enough revenue to cover the costs to fulfill the redemptions, causing a reduction in income during this period. Eventually, a true up of the liability may be needed to reflect a more accurate URR. This can be quite painful for companies with large liabilities. As noted earlier, even a small restatement of the liability can impact income by tens of millions of dollars.


Obviously, the outcome of having a URR estimate that is either too high or too low is not desirable. The nature of such risks often results in tough questions by senior leaders and auditors on the state of the company’s loyalty program liability. Having a robust analytic framework that uses sophisticated modeling rooted in actuarial theory, along with leveraging predictive modeling tools, helps mitigate risk and proves to these stakeholders that your estimate are accurate.


Bottom line

Proper accounting and financial reporting of your liability requires an accurate estimate of the ultimate redemption rate and cost per point. One powerful way to accomplish this is to integrate actuarial science with advanced computational capacities of modern predictive modeling techniques.



What finance departments need to know

Though loss of cash and an increase in liability is hardly appealing to the finance department, finding the proper balance of customer engagement needs to be strategically executed for sustained competitive standing.

Loyalty Program Finance

It’s important to note that the financial impact of issuing rewards points is not incurred at the moment at which they’re redeemed, but, rather, at the time of their issuance. The second the rewards points are doled out to participants, the company incurs the accompanying costs associated with “potentially redeemable points,” either as a reduction in revenue or as a direct recognition of expense, depending on how the program is accounted for.


While accounting is often focused on current liability estimates, many in loyalty finance roles are focused on future liability (i.e., how the liability will grow over time). And to accurately predict future liability, finance must have a solid understand of URR and CPP, too.


It’s also important for finance teams to recognize that, as user engagement increases and members graduate from being casual participants to more heavily invested users, rates of redemption will fluctuate upwards. This, of course, can be offset by the arrival of more new members, whose engagement is typically less vigorous.


This means that it should be expected that the URR will change over time. Failure to recognize this in your financial planning could result in material variance in financial performance.


The trajectory of the liability is also influenced by loyalty program changes and loyalty campaigns. Understanding how changes in these programs, such as modifications to expiration rules or earning rules, or the addition of a new co-branded credit card, impacts the URR and CPP is critical to building an accurate financial plan.


A sole focus on costs may drive some to wish for high breakage. This one dimensional view should be avoided. Program managers must be wary of trying to encourage an excess of breakage, as doing so involves intentionally disengaging customers from the company.


Best practice is for companies to focus not just on liability, but more holistically on customer lifetime value (CLV). CLV considers both the cost of redemptions, as well as the revenue generated from a lifetime of loyalty from your customers. This is the most important metric for any loyalty program.


Cost considerations for CLV include items such as acquisition costs and redemption costs. Therefore, the ultimate redemption rate and cost per point are critical to understanding CLV.


The other half of the CLV calculation is related to revenue — in particular, expected future revenue. Unlike liability, expected future revenue from your members is not an asset you can put on your balance sheet, and is a big reason why there is so much focus on cost.


CLV puts liability in the appropriate context. Program strategies may increase the URR, and therefore increase the liability. But if the expected future revenue sufficiently increases more than expected future costs, then the strategy is a smart financial choice. Disciplined loyalty finance professionals should insist on quantifying CLV to fully understand the financial health of their program.


Bottom line

Ensuring accurate loyalty program liability is not only critical to satisfying Wall Street’s demand for accurate financial forecasts, but for measuring loyalty program ROI as a whole. The challenge for the finance team, then, is to get this right amidst the technical difficulties of implementing precise predictive models and constantly evolving loyalty program marketing strategies.



What marketing teams should know about loyalty program liability

Marketers can get broader buy in and investment in their loyalty initiatives by accurately quantifying liability and CLV.

Loyalty Program Marketing

Marketing departments are responsible for the way in which a company engages with its clientele, and are the vehicle through which customer engagement is controlled. When it comes to loyalty programs, these levels of engagement predict corresponding levels of redemption. This means that marketing plays a key role in managing loyalty program liability.


For the most part, a marketer’s primary focus is not going to be program liability. And it shouldn’t be. With that said, they still have stakeholders in finance and accounting that are concerned about it. Understanding the financial implications of their engagement strategies will help get broad buy-in across departments.


Increasing breakage rates indicates a lack of engagement by members and demonstrates that customers don’t see the program as having value. While it may be beneficial for a company to dump its liability in the short run, this will not be a sustainable strategy for long-term customer engagement. It’s safe to assume that most loyalty professionals, regardless if they’re sitting in finance, accounting or marketing, know this to be true.


The challenge for many loyalty marketers, then, is that business cases often require sound logic and quantifiable evidence. This is where accurate liability estimates and CLV are helpful. If marketers can show that their chosen strategy will sufficiently increase CLV, this shows quantifiable evidence indicating that increasing liability will generate the needed ROI. It’s evidence that marketing, finance and accounting can all get behind.


Beyond building the financial case for a given strategy, CLV can also be used to help identify opportunities and new strategies. This is particularly true when CLV is estimated at the individual member level. This allows you to quantify and identify your most valuable members based on their expected future value, rather than their historical behavior.


This predictive view will have the biggest impact on future profit potential. Focusing your efforts and resources on these opportunities will maximize program ROI.


Bottom line

Marketers, finance professionals and accountants are all key stakeholders in a thriving loyalty program. The key metric at the intersection of their objectives is CLV. Accurate CLV requires an accurate estimate of the URR, CPP and program liability.


All loyalty professionals should demand predictive CLV and, consequently, demand accurate liability estimation.



Final thoughts: Keep your business sustainable

Regardless of where you’re sitting in a loyalty program, you need an accurate estimation of  ultimate redemption rate, cost per point, and loyalty program liability.


For accountants, this means needing to comply with financial reporting requirements.


For finance, this means building an accurate financial plan that ensures that smart financial decisions are being made.


For marketing, this means framing programs and campaigns in the context of how they affect liability and customer lifetime value to get needed buy in from accounting and finance.


While all companies must estimate URR, CPP and liability for financial reporting, disciplined loyalty professionals should not stop there. They should insist on evolving those models to provide accurate customer lifetime value estimation.


And accurate CLV cannot be calculated without first understanding URR and CPP at a granular member level. Accurate liability is the starting point.




KYROS provides sophisticated predictive analytics solutions that help companies optimize the financial performance of their loyalty program. Want to maximize the economic value of your program?  Contact us  for a free consultation.


Customer loyalty, predicted

For a free 30 minute introductory consultation, please fill out the fields below

Your Name (required)

Your Email (required)

Your Message

Customer loyalty, predicted

For a free 30 minute introductory consultation, please fill out the fields below

Your Name (required)

Your Email (required)

Your Message

Customer loyalty, predicted

For a free 30 minute introductory consultation, please fill out the fields below

Your Name (required)

Your Email (required)

Your Message

Customer loyalty, predicted.

For a free 30 minute introductory consultation, please fill out the fields below.

Your Name (required)

Your Email (required)

Your Message

Thank you for contacting Kyros.

We will get in touch with you shortly.